Overview

Employment Type: Full-Time

Responsibilities

• Data Science and Analytics
• Collect, clean, process, and analyze structured and unstructured datasets to identify patterns, trends, and actionable business insights.
• Perform exploratory data analysis, statistical analysis, hypothesis testing, and feature engineering to support AI and machine learning initiatives.
• Machine Learning Model Development
• Design, develop, train, and optimize machine learning models for classification, regression, clustering, forecasting, and recommendation systems.
• Evaluate model performance using appropriate metrics and improve accuracy, reliability, and generalization through experimentation and tuning.
• AI and Generative AI Development
• Develop AI-powered applications using generative AI, Large Language Models (LLMs), natural language processing (NLP), and other relevant AI techniques.
• Implement solutions using prompt engineering, embeddings, Retrieval-Augmented Generation (RAG), and vector databases where appropriate.
• Deep Learning and Advanced AI
• Build and deploy deep learning models using frameworks such as PyTorch or TensorFlow for suitable business use cases.
• Explore and implement advanced AI techniques, including text processing, intelligent automation, and computer vision where required.
• Data Processing and Pipeline Development
• Develop efficient data preprocessing, transformation, and feature engineering pipelines using Python, SQL, and relevant data processing tools.
• Work with large datasets and collaborate with data engineering teams to ensure data quality, consistency, and availability for AI model development.
• Model Deployment and MLOps
• Deploy machine learning models and AI applications into production using REST APIs, FastAPI, Flask, or equivalent technologies.
• Implement model versioning, experiment tracking, automated deployment, performance monitoring, and retraining workflows using tools such as MLflow, Docker, and CI/CD pipelines.

Qualifications

• Employment Type: Full-Time
• Work Type: Remote
• Job Summary
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Statistics, Mathematics, or a related field.
• 5+ years of professional experience in AI engineering, machine learning engineering, data science, or a closely related role.
• Strong programming skills in Python and SQL, including data manipulation, analysis, and efficient application development.
• Hands-on experience with machine learning algorithms, statistical modeling, feature engineering, model evaluation, and optimization.
• Experience with Python libraries and frameworks such as Pandas, NumPy, Scikit-learn, and SciPy.
• Practical experience with deep learning frameworks such as PyTorch or TensorFlow.
• Experience developing, deploying, and integrating AI or machine learning models into production applications.
• Understanding of REST APIs, FastAPI, Flask, or similar frameworks for serving AI models and integrating AI capabilities into software products.
• Working knowledge of MLOps practices, including experiment tracking, model versioning, deployment automation, and model monitoring.
• Familiarity with Git, Docker, CI/CD workflows, and software engineering best practices.
• Strong analytical thinking, problem-solving abilities, and communication skills.

Nice to have

• Hands-on experience with LLMs, generative AI, prompt engineering, RAG architectures, embeddings, and vector databases.
• Experience with cloud-based AI/ML services such as Azure Machine Learning, Azure OpenAI, AWS SageMaker, or Google Vertex AI.
• Familiarity with MLflow, Kubeflow, Kubernetes, or other tools for model lifecycle management and scalable deployment.
• Experience with Apache Spark, Databricks, or distributed data processing frameworks.
• Knowledge of Power BI, Tableau, or Python-based visualization tools for presenting analytical findings.
• Understanding of model optimization techniques, data drift detection, model observability, and production performance monitoring.